# Content Creation with Spillovers: An Incentive Design Approach

arXiv: https://arxiv.org/abs/2603.14372 · HTML: https://mlsanity.com/paper/2603.14372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 15 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023)
- [A Constructive Prediction of the Generalization Error Across Scales](https://mlsanity.com/paper/1909.12673/) (2019)
- [Scaling Retrieval-Based Language Models with a Trillion-Token Datastore](https://mlsanity.com/paper/2407.12854/) (2024)
- [Modeling Content Creator Incentives on Algorithm-Curated Platforms](https://mlsanity.com/paper/2206.13102/) (2022)
- [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018)
- [Supply-Side Equilibria in Recommender Systems](https://mlsanity.com/paper/2206.13489/) (2022)
- [Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?](https://mlsanity.com/paper/2402.15467/) (2024)
- [From Recommendation Systems to Facility Location Games](https://mlsanity.com/paper/1809.02931/) (2018)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms](https://mlsanity.com/paper/2410.23683/) (2024)
- [How to Strategize Human Content Creation in the Era of GenAI?](https://mlsanity.com/paper/2406.05187/) (2024)
- [Networked Digital Public Goods Games with Heterogeneous Players and Convex Costs](https://mlsanity.com/paper/2502.01001/) (2025)
- [Collaborating with GenAI: Incentives and Replacements](https://mlsanity.com/paper/2508.20213/) (2025)

## Abstract (arXiv)

The rise of AI amplifies the economic phenomenon of positive spillovers: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination. While such spillovers can improve content quality, they also reshape incentives, as creators may reduce effort when they can benefit from others' contributions. We introduce the Content Creation with Spillovers (CCS) model, a game between a platform and strategic creators. In this game, each creator chooses an effort level, and qualities are jointly determined by all creators' efforts. The platform contracts on qualities while seeking to maximize social welfare. We show that standard contest mechanisms can be unstable, and propose a parametrized family of Provisional Allocation mechanisms that guarantee a Pareto-dominant equilibrium. Although optimizing welfare within this family is hard to approximate in general, we develop approximation algorithms whose guarantees hold either deterministically across structured spillover classes or with high probability under random instances.